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Enregistrement W2956058033 · doi:10.7189/jogh.09.010812

Clinical evaluation of the use of an mhealth intervention on quality of care provided by Community Health Workers in southwest Niger

2019· article· en· W2956058033 sur OpenAlexafffundabout
David Zakus, Moise Moussa, Mahamane Ezechiel, Joannes Paulus Yimbesalu, Patsy Orkar, Caroline Damecour, Annette E Ghee, Matthew D Macfarlane, Grace Nganga

Notice bibliographique

RevueJournal of Global Health · 2019
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMobile Health and mHealth Applications
Établissements canadiensWorld Wildlife Fund CanadaYork UniversityUniversity of Toronto
Organismes subventionnairesGlobal Affairs CanadaWorld Health Organization
Mots-clésMedicinemHealthRandomized controlled trialFamily medicineIntervention (counseling)Health careHealth facilityRural areaEnvironmental healthNursingPsychological interventionPopulation

Résumé

récupéré en direct d'OpenAlex

Clinical evaluation of the use of an mhealth intervention on quality of care provided by Community Health Workers in southwest NigerBackground Under the World Health Organization' s (WHO) integrated community case management (iCCM) Rapid Access Expansion Program (RAcE), World Vision Niger and Canada supported the Niger Ministry of Public Health to implement iCCM in four health districts in Niger in 2013.Community health workers (CHWs), known as Relais Communautaire (RCom), were deployed in their communities to diagnose and treat children under five years of age presenting with diarrhea, malaria and pneumonia and refer children with severe illness to the higher-level facilities.Two of the districts in southwest Niger piloted RCom using smartphones equipped with an application to support quality case management and provide good timely clinical data.A two-arm cluster randomized trial assessed the impact of use of the mHealth application mainly on quality of care (QoC), but also on motivation, retention and supervision.Methods A two-arm cluster randomized trial was conducted from March to October 2016 in Dosso and Doutchi districts.The intervention arm comprised 66 RCom equipped with a smartphone and 64 in the paper-based control arm.Trained expert clinicians observed each RCom assessing sick children presenting to them (264 in intervention group; 256 in control group), re-assessed each child on the same set of parameters, and made further observations regarding perceptions of motivation, retention, supervision, drug management and caregiver satisfaction.The primary outcome was a QoC score composed of diagnostic and treatment variables.Other factors were assessed by questionnaires.Results On average, the mHealth equipped RCom showed a 3.4% higher QoC score (mean difference of 0.83 points).They were more likely to ask about the main danger signs: convulsions (69.7% vs 50.4%,P < 0.001); incapacity to drink or eat (79.2% vs 59.4%, P < 0.001); vomiting (81.4% vs 69.9%, P < 0.01); and lethargy or unconsciousness (92.4% vs 84.8%, P < 0.01).Specifically, they consistently asked one more screening question.They were also significantly better at examining for swelling feet (40.2% vs 13.3%, P < 0.01) and advising caretakers on diarrhea, drug dosage and administration, and performed (though non-significantly) better when examining cough and breathing rates, referring all conditions, getting children to take prescribed treatments immediately and having caregivers understand treatment continuation.The control group was significantly better at diagnosing fast breathing, bloody diarrhea and severe acute malnutrition; and was somewhat better (non-significant) at treating fever and malaria.With treatment in general of the three diseases, there was no significant difference between the groups.On further inspection, 83% of the intervention group had a QoC score greater than 80% (25 out of 31), whereas only 67% of the control group had comparable performance.With respect to referrals, the intervention group performed better, mostly based on their better assessment of danger signs, with more correct (85% vs 29%) and fewer missed, plus a lower proportion of incorrect referrals, with the reverse being true for the controls (P = 0.012).There were no statistically significant differences in motivation, retention and supervision between the two groups, yet intervention RCom reported double the rate of no supervision in the last three months (31.8% vs 15.6%).Conclusions Results suggest that use of the mHealth application led to modestly improved QoC through better assessment of the sick children and better referral decisions by RCom, but not to improvement in the actual treatment of malaria, pneumonia and diarrhea.Considering mHealth' s additional costs and logistics, questions around its viability remain.Further implementation could be improved by investing in RCom capacity building, building organization culture and strengthened supervision, all essential areas for improving any CHW program.In this real-world setting, in poor and remote communities in rural Niger, this study did not support the overall value of the mHealth intervention.Much was learned for any future mHealth interventions and scale-up.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,022

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,245
Tête enseignante GPT0,593
Écart entre enseignants0,348 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations26
Publié2019
Routes d'admission3
Résumé présentoui

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